Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations168
Missing cells34
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.8 KiB
Average record size in memory248.8 B

Variable types

Numeric1
Categorical16
Text14

Alerts

año is highly overall correlated with calidad_de_agua and 12 other fieldsHigh correlation
calidad_de_agua is highly overall correlated with año and 1 other fieldsHigh correlation
campaña is highly overall correlated with año and 5 other fieldsHigh correlation
cd_total_mg_l is highly overall correlated with año and 10 other fieldsHigh correlation
codigo is highly overall correlated with orden and 1 other fieldsHigh correlation
color is highly overall correlated with año and 8 other fieldsHigh correlation
cr_total_mg_l is highly overall correlated with año and 6 other fieldsHigh correlation
espumas is highly overall correlated with año and 9 other fieldsHigh correlation
fecha is highly overall correlated with año and 5 other fieldsHigh correlation
hidr_deriv_petr_ug_l is highly overall correlated with año and 9 other fieldsHigh correlation
ica is highly overall correlated with año and 3 other fieldsHigh correlation
mat_susp is highly overall correlated with año and 7 other fieldsHigh correlation
microcistina_ug_l is highly overall correlated with año and 7 other fieldsHigh correlation
olores is highly overall correlated with año and 9 other fieldsHigh correlation
orden is highly overall correlated with codigo and 1 other fieldsHigh correlation
sitios is highly overall correlated with codigo and 1 other fieldsHigh correlation
tem_aire is highly overall correlated with año and 7 other fieldsHigh correlation
año is highly imbalanced (86.5%)Imbalance
cr_total_mg_l is highly imbalanced (52.3%)Imbalance
cd_total_mg_l is highly imbalanced (64.4%)Imbalance
tem_aire has 2 (1.2%) missing valuesMissing
ica has 13 (7.7%) missing valuesMissing
calidad_de_agua has 14 (8.3%) missing valuesMissing
sitios is uniformly distributedUniform
codigo is uniformly distributedUniform

Reproduction

Analysis started2024-10-02 23:45:22.238927
Analysis finished2024-10-02 23:45:23.936507
Duration1.7 second
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

orden
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:24.001352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21.5
Q332
95-th percentile40
Maximum42
Range41
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.157155
Coefficient of variation (CV)0.56544905
Kurtosis-1.2013151
Mean21.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum3612
Variance147.79641
MonotonicityNot monotonic
2024-10-02T20:45:24.100151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 4
 
2.4%
2 4
 
2.4%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
8 4
 
2.4%
9 4
 
2.4%
10 4
 
2.4%
Other values (32) 128
76.2%
ValueCountFrequency (%)
1 4
2.4%
2 4
2.4%
3 4
2.4%
4 4
2.4%
5 4
2.4%
6 4
2.4%
7 4
2.4%
8 4
2.4%
9 4
2.4%
10 4
2.4%
ValueCountFrequency (%)
42 4
2.4%
41 4
2.4%
40 4
2.4%
39 4
2.4%
38 4
2.4%
37 4
2.4%
36 4
2.4%
35 4
2.4%
34 4
2.4%
33 4
2.4%

sitios
Categorical

HIGH CORRELATION  UNIFORM 

Distinct42
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Canal Villanueva y Río Luján
 
4
Río Lujan y Arroyo Caraguatá
 
4
Canal Aliviador y Río Lujan
 
4
Río Carapachay y Arroyo Gallo Fiambre
 
4
Río Reconquista y Río Lujan
 
4
Other values (37)
148 

Length

Max length41
Median length27.5
Mean length23.238095
Min length8

Characters and Unicode

Total characters3904
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowRío Lujan y Arroyo Caraguatá
3rd rowCanal Aliviador y Río Lujan
4th rowRío Carapachay y Arroyo Gallo Fiambre
5th rowRío Reconquista y Río Lujan

Common Values

ValueCountFrequency (%)
Canal Villanueva y Río Luján 4
 
2.4%
Río Lujan y Arroyo Caraguatá 4
 
2.4%
Canal Aliviador y Río Lujan 4
 
2.4%
Río Carapachay y Arroyo Gallo Fiambre 4
 
2.4%
Río Reconquista y Río Lujan 4
 
2.4%
Rio Tigre 100m antes del Rio Luján 4
 
2.4%
Río Lujan y Canal San Fernando 4
 
2.4%
Río Capitán y Río San Antonio 4
 
2.4%
Arroyo Abra Vieja y Santa Rosa 4
 
2.4%
Del Arca 4
 
2.4%
Other values (32) 128
76.2%

Length

2024-10-02T20:45:24.207147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 40
 
5.9%
río 36
 
5.3%
de 32
 
4.7%
arroyo 24
 
3.5%
espigón 16
 
2.4%
lujan 16
 
2.4%
canal 12
 
1.8%
la 12
 
1.8%
playa 12
 
1.8%
reserva 12
 
1.8%
Other values (94) 468
68.8%

Most occurring characters

ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
512
 
13.1%
a 476
 
12.2%
o 312
 
8.0%
e 228
 
5.8%
r 224
 
5.7%
l 192
 
4.9%
n 192
 
4.9%
i 160
 
4.1%
s 120
 
3.1%
c 108
 
2.8%
Other values (47) 1380
35.3%

codigo
Categorical

HIGH CORRELATION  UNIFORM 

Distinct42
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
TI001
 
4
TI006
 
4
TI002
 
4
TI003
 
4
TI004
 
4
Other values (37)
148 

Length

Max length8
Median length5
Mean length5.0714286
Min length5

Characters and Unicode

Total characters852
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTI001
2nd rowTI006
3rd rowTI002
4th rowTI003
5th rowTI004

Common Values

ValueCountFrequency (%)
TI001 4
 
2.4%
TI006 4
 
2.4%
TI002 4
 
2.4%
TI003 4
 
2.4%
TI004 4
 
2.4%
TI005 4
 
2.4%
TI007 4
 
2.4%
TI008 4
 
2.4%
TI009 4
 
2.4%
SF015 4
 
2.4%
Other values (32) 128
76.2%

Length

2024-10-02T20:45:24.308496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ti001 4
 
2.4%
ti006 4
 
2.4%
ti002 4
 
2.4%
ti003 4
 
2.4%
ti004 4
 
2.4%
ti005 4
 
2.4%
ti007 4
 
2.4%
ti008 4
 
2.4%
ti009 4
 
2.4%
sf015 4
 
2.4%
Other values (32) 128
76.2%

Most occurring characters

ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 204
23.9%
I 52
 
6.1%
2 44
 
5.2%
S 40
 
4.7%
4 40
 
4.7%
3 40
 
4.7%
T 36
 
4.2%
1 36
 
4.2%
5 36
 
4.2%
A 36
 
4.2%
Other values (20) 288
33.8%

fecha
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
23/2/2022
42 
4/5/2022
42 
23/8/2022
42 
31/10/2022
35 
no midieron este día
 
4
Other values (2)
 
3

Length

Max length20
Median length15.5
Mean length9.2440476
Min length8

Characters and Unicode

Total characters1553
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row23/2/2022
2nd row23/2/2022
3rd row23/2/2022
4th row23/2/2022
5th row23/2/2022

Common Values

ValueCountFrequency (%)
23/2/2022 42
25.0%
4/5/2022 42
25.0%
23/8/2022 42
25.0%
31/10/2022 35
20.8%
no midieron este día 4
 
2.4%
31/10/0202 2
 
1.2%
no se midió 1
 
0.6%

Length

2024-10-02T20:45:24.406270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:24.510434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
23/2/2022 42
23.1%
4/5/2022 42
23.1%
23/8/2022 42
23.1%
31/10/2022 35
19.2%
no 5
 
2.7%
midieron 4
 
2.2%
este 4
 
2.2%
día 4
 
2.2%
31/10/0202 2
 
1.1%
se 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 613
39.5%
/ 326
21.0%
0 202
 
13.0%
3 121
 
7.8%
1 74
 
4.8%
4 42
 
2.7%
5 42
 
2.7%
8 42
 
2.7%
14
 
0.9%
e 13
 
0.8%
Other values (11) 64
 
4.1%

año
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2022
163 
no midieron este día
 
4
no se midió
 
1

Length

Max length20
Median length4
Mean length4.422619
Min length4

Characters and Unicode

Total characters743
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 163
97.0%
no midieron este día 4
 
2.4%
no se midió 1
 
0.6%

Length

2024-10-02T20:45:24.630256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:24.724501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 163
89.6%
no 5
 
2.7%
midieron 4
 
2.2%
este 4
 
2.2%
día 4
 
2.2%
se 1
 
0.5%
midió 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 489
65.8%
0 163
 
21.9%
14
 
1.9%
e 13
 
1.7%
i 10
 
1.3%
o 9
 
1.2%
n 9
 
1.2%
d 9
 
1.2%
m 5
 
0.7%
s 5
 
0.7%
Other values (5) 17
 
2.3%

campaña
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Verano
42 
otoño
42 
invierno
42 
Primavera
37 
no midieron este día
 
4

Length

Max length20
Median length10
Mean length7.2738095
Min length5

Characters and Unicode

Total characters1222
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowVerano
2nd rowVerano
3rd rowVerano
4th rowVerano
5th rowVerano

Common Values

ValueCountFrequency (%)
Verano 42
25.0%
otoño 42
25.0%
invierno 42
25.0%
Primavera 37
22.0%
no midieron este día 4
 
2.4%
no se midió 1
 
0.6%

Length

2024-10-02T20:45:24.820196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:24.920028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
verano 42
23.1%
otoño 42
23.1%
invierno 42
23.1%
primavera 37
20.3%
no 5
 
2.7%
midieron 4
 
2.2%
este 4
 
2.2%
día 4
 
2.2%
se 1
 
0.5%
midió 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 219
17.9%
r 162
13.3%
n 135
11.0%
e 134
11.0%
i 131
10.7%
a 120
9.8%
v 79
 
6.5%
t 46
 
3.8%
V 42
 
3.4%
ñ 42
 
3.4%
Other values (7) 112
9.2%

tem_agua
Text

Distinct93
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:25.114762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length4.7202381
Min length1

Characters and Unicode

Total characters793
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)36.3%

Sample

1st row24.5
2nd row25.4
3rd row24.6
4th row25.2
5th row24.1
ValueCountFrequency (%)
no 23
 
10.6%
se 19
 
8.7%
midió 19
 
8.7%
10 7
 
3.2%
20 6
 
2.8%
18.5 5
 
2.3%
18.6 5
 
2.3%
17 4
 
1.8%
midieron 4
 
1.8%
este 4
 
1.8%
Other values (87) 122
56.0%
2024-10-02T20:45:25.403579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 114
14.4%
. 106
13.4%
2 59
 
7.4%
50
 
6.3%
i 46
 
5.8%
4 39
 
4.9%
6 32
 
4.0%
e 31
 
3.9%
5 31
 
3.9%
8 31
 
3.9%
Other values (14) 254
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 114
14.4%
. 106
13.4%
2 59
 
7.4%
50
 
6.3%
i 46
 
5.8%
4 39
 
4.9%
6 32
 
4.0%
e 31
 
3.9%
5 31
 
3.9%
8 31
 
3.9%
Other values (14) 254
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 114
14.4%
. 106
13.4%
2 59
 
7.4%
50
 
6.3%
i 46
 
5.8%
4 39
 
4.9%
6 32
 
4.0%
e 31
 
3.9%
5 31
 
3.9%
8 31
 
3.9%
Other values (14) 254
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 114
14.4%
. 106
13.4%
2 59
 
7.4%
50
 
6.3%
i 46
 
5.8%
4 39
 
4.9%
6 32
 
4.0%
e 31
 
3.9%
5 31
 
3.9%
8 31
 
3.9%
Other values (14) 254
32.0%

tem_aire
Categorical

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)19.3%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
14
25 
no se midió
18 
13
15 
12
12 
16
11 
Other values (27)
85 

Length

Max length20
Median length2
Mean length3.5421687
Min length1

Characters and Unicode

Total characters588
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)5.4%

Sample

1st row23.3
2nd row23.3
3rd row23.3
4th row23.3
5th row20

Common Values

ValueCountFrequency (%)
14 25
14.9%
no se midió 18
 
10.7%
13 15
 
8.9%
12 12
 
7.1%
16 11
 
6.5%
17 8
 
4.8%
15 7
 
4.2%
27 6
 
3.6%
22 6
 
3.6%
23.3 5
 
3.0%
Other values (22) 53
31.5%

Length

2024-10-02T20:45:25.516253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14 25
 
11.7%
no 22
 
10.3%
se 18
 
8.4%
midió 18
 
8.4%
13 15
 
7.0%
12 12
 
5.6%
16 11
 
5.1%
17 8
 
3.7%
15 7
 
3.3%
27 6
 
2.8%
Other values (26) 72
33.6%

Most occurring characters

ValueCountFrequency (%)
1 105
17.9%
2 62
 
10.5%
48
 
8.2%
i 44
 
7.5%
3 31
 
5.3%
e 30
 
5.1%
o 26
 
4.4%
4 26
 
4.4%
n 26
 
4.4%
d 26
 
4.4%
Other values (14) 164
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 105
17.9%
2 62
 
10.5%
48
 
8.2%
i 44
 
7.5%
3 31
 
5.3%
e 30
 
5.1%
o 26
 
4.4%
4 26
 
4.4%
n 26
 
4.4%
d 26
 
4.4%
Other values (14) 164
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 105
17.9%
2 62
 
10.5%
48
 
8.2%
i 44
 
7.5%
3 31
 
5.3%
e 30
 
5.1%
o 26
 
4.4%
4 26
 
4.4%
n 26
 
4.4%
d 26
 
4.4%
Other values (14) 164
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 105
17.9%
2 62
 
10.5%
48
 
8.2%
i 44
 
7.5%
3 31
 
5.3%
e 30
 
5.1%
o 26
 
4.4%
4 26
 
4.4%
n 26
 
4.4%
d 26
 
4.4%
Other values (14) 164
27.9%

od
Text

Distinct130
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:25.718067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.6130952
Min length1

Characters and Unicode

Total characters943
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)71.4%

Sample

1st row5.3
2nd row2.25
3rd row2.94
4th row2.22
5th row1.02
ValueCountFrequency (%)
no 36
 
14.6%
se 29
 
11.7%
midió 29
 
11.7%
midieron 4
 
1.6%
este 4
 
1.6%
día 4
 
1.6%
midio 3
 
1.2%
la 3
 
1.2%
sonda 3
 
1.2%
5.36 2
 
0.8%
Other values (125) 130
52.6%
2024-10-02T20:45:26.040765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 124
 
13.1%
79
 
8.4%
i 72
 
7.6%
o 46
 
4.9%
8 46
 
4.9%
1 45
 
4.8%
5 45
 
4.8%
d 43
 
4.6%
e 41
 
4.3%
7 40
 
4.2%
Other values (16) 362
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
 
13.1%
79
 
8.4%
i 72
 
7.6%
o 46
 
4.9%
8 46
 
4.9%
1 45
 
4.8%
5 45
 
4.8%
d 43
 
4.6%
e 41
 
4.3%
7 40
 
4.2%
Other values (16) 362
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
 
13.1%
79
 
8.4%
i 72
 
7.6%
o 46
 
4.9%
8 46
 
4.9%
1 45
 
4.8%
5 45
 
4.8%
d 43
 
4.6%
e 41
 
4.3%
7 40
 
4.2%
Other values (16) 362
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
 
13.1%
79
 
8.4%
i 72
 
7.6%
o 46
 
4.9%
8 46
 
4.9%
1 45
 
4.8%
5 45
 
4.8%
d 43
 
4.6%
e 41
 
4.3%
7 40
 
4.2%
Other values (16) 362
38.4%

ph
Text

Distinct116
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:26.236721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.1845238
Min length1

Characters and Unicode

Total characters871
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)57.7%

Sample

1st row6.56
2nd row6.56
3rd row6.59
4th row7.45
5th row6.39
ValueCountFrequency (%)
no 28
 
12.3%
se 24
 
10.5%
midió 23
 
10.1%
7.4 6
 
2.6%
7.6 4
 
1.8%
midieron 4
 
1.8%
este 4
 
1.8%
día 4
 
1.8%
7.76 3
 
1.3%
7,67 3
 
1.3%
Other values (110) 125
54.8%
2024-10-02T20:45:26.531598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 108
 
12.4%
7 103
 
11.8%
60
 
6.9%
i 55
 
6.3%
8 51
 
5.9%
6 47
 
5.4%
e 38
 
4.4%
n 33
 
3.8%
o 32
 
3.7%
5 32
 
3.7%
Other values (15) 312
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 108
 
12.4%
7 103
 
11.8%
60
 
6.9%
i 55
 
6.3%
8 51
 
5.9%
6 47
 
5.4%
e 38
 
4.4%
n 33
 
3.8%
o 32
 
3.7%
5 32
 
3.7%
Other values (15) 312
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 108
 
12.4%
7 103
 
11.8%
60
 
6.9%
i 55
 
6.3%
8 51
 
5.9%
6 47
 
5.4%
e 38
 
4.4%
n 33
 
3.8%
o 32
 
3.7%
5 32
 
3.7%
Other values (15) 312
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 108
 
12.4%
7 103
 
11.8%
60
 
6.9%
i 55
 
6.3%
8 51
 
5.9%
6 47
 
5.4%
e 38
 
4.4%
n 33
 
3.8%
o 32
 
3.7%
5 32
 
3.7%
Other values (15) 312
35.8%

olores
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Ausencia
120 
Ausente
16 
Presencia
 
12
no se midió
 
9
ausencia
 
7

Length

Max length20
Median length8
Mean length8.422619
Min length7

Characters and Unicode

Total characters1415
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAusencia
2nd rowPresencia
3rd rowAusencia
4th rowPresencia
5th rowAusencia

Common Values

ValueCountFrequency (%)
Ausencia 120
71.4%
Ausente 16
 
9.5%
Presencia 12
 
7.1%
no se midió 9
 
5.4%
ausencia 7
 
4.2%
no midieron este día 4
 
2.4%

Length

2024-10-02T20:45:26.650405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:26.753703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ausencia 127
64.1%
ausente 16
 
8.1%
no 13
 
6.6%
presencia 12
 
6.1%
se 9
 
4.5%
midió 9
 
4.5%
midieron 4
 
2.0%
este 4
 
2.0%
día 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 204
14.4%
n 172
12.2%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 143
10.1%
c 139
9.8%
A 136
9.6%
30
 
2.1%
t 20
 
1.4%
Other values (7) 88
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 204
14.4%
n 172
12.2%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 143
10.1%
c 139
9.8%
A 136
9.6%
30
 
2.1%
t 20
 
1.4%
Other values (7) 88
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 204
14.4%
n 172
12.2%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 143
10.1%
c 139
9.8%
A 136
9.6%
30
 
2.1%
t 20
 
1.4%
Other values (7) 88
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 204
14.4%
n 172
12.2%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 143
10.1%
c 139
9.8%
A 136
9.6%
30
 
2.1%
t 20
 
1.4%
Other values (7) 88
6.2%

color
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Ausencia
119 
Ausente
16 
Presencia
13 
no se midió
 
9
ausencia
 
7

Length

Max length20
Median length8
Mean length8.4285714
Min length7

Characters and Unicode

Total characters1416
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAusencia
2nd rowPresencia
3rd rowPresencia
4th rowPresencia
5th rowPresencia

Common Values

ValueCountFrequency (%)
Ausencia 119
70.8%
Ausente 16
 
9.5%
Presencia 13
 
7.7%
no se midió 9
 
5.4%
ausencia 7
 
4.2%
no midieron este día 4
 
2.4%

Length

2024-10-02T20:45:27.056622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:27.149716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ausencia 126
63.6%
ausente 16
 
8.1%
presencia 13
 
6.6%
no 13
 
6.6%
se 9
 
4.5%
midió 9
 
4.5%
midieron 4
 
2.0%
este 4
 
2.0%
día 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 205
14.5%
n 172
12.1%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 142
10.0%
c 139
9.8%
A 135
9.5%
30
 
2.1%
t 20
 
1.4%
Other values (7) 90
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 205
14.5%
n 172
12.1%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 142
10.0%
c 139
9.8%
A 135
9.5%
30
 
2.1%
t 20
 
1.4%
Other values (7) 90
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 205
14.5%
n 172
12.1%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 142
10.0%
c 139
9.8%
A 135
9.5%
30
 
2.1%
t 20
 
1.4%
Other values (7) 90
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 205
14.5%
n 172
12.1%
s 168
11.9%
i 165
11.7%
a 150
10.6%
u 142
10.0%
c 139
9.8%
A 135
9.5%
30
 
2.1%
t 20
 
1.4%
Other values (7) 90
6.4%

espumas
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Ausencia
127 
Ausente
16 
no se midió
 
9
ausencia
 
7
Presencia
 
5

Length

Max length20
Median length8
Mean length8.3809524
Min length7

Characters and Unicode

Total characters1408
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAusencia
2nd rowAusencia
3rd rowAusencia
4th rowAusencia
5th rowAusencia

Common Values

ValueCountFrequency (%)
Ausencia 127
75.6%
Ausente 16
 
9.5%
no se midió 9
 
5.4%
ausencia 7
 
4.2%
Presencia 5
 
3.0%
no midieron este día 4
 
2.4%

Length

2024-10-02T20:45:27.257572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:27.352832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ausencia 134
67.7%
ausente 16
 
8.1%
no 13
 
6.6%
se 9
 
4.5%
midió 9
 
4.5%
presencia 5
 
2.5%
midieron 4
 
2.0%
este 4
 
2.0%
día 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 197
14.0%
n 172
12.2%
s 168
11.9%
i 165
11.7%
u 150
10.7%
a 150
10.7%
A 143
10.2%
c 139
9.9%
30
 
2.1%
t 20
 
1.4%
Other values (7) 74
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 197
14.0%
n 172
12.2%
s 168
11.9%
i 165
11.7%
u 150
10.7%
a 150
10.7%
A 143
10.2%
c 139
9.9%
30
 
2.1%
t 20
 
1.4%
Other values (7) 74
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 197
14.0%
n 172
12.2%
s 168
11.9%
i 165
11.7%
u 150
10.7%
a 150
10.7%
A 143
10.2%
c 139
9.9%
30
 
2.1%
t 20
 
1.4%
Other values (7) 74
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 197
14.0%
n 172
12.2%
s 168
11.9%
i 165
11.7%
u 150
10.7%
a 150
10.7%
A 143
10.2%
c 139
9.9%
30
 
2.1%
t 20
 
1.4%
Other values (7) 74
 
5.3%

mat_susp
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Ausencia
106 
Presencia
26 
Ausente
16 
no se midió
 
9
ausencia
 
6
Other values (2)
 
5

Length

Max length20
Median length8
Mean length8.5119048
Min length7

Characters and Unicode

Total characters1430
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowPresencia
2nd rowAusencia
3rd rowAusencia
4th rowAusencia
5th rowPresencia

Common Values

ValueCountFrequency (%)
Ausencia 106
63.1%
Presencia 26
 
15.5%
Ausente 16
 
9.5%
no se midió 9
 
5.4%
ausencia 6
 
3.6%
no midieron este día 4
 
2.4%
presencia 1
 
0.6%

Length

2024-10-02T20:45:27.458175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:27.554079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ausencia 112
56.6%
presencia 27
 
13.6%
ausente 16
 
8.1%
no 13
 
6.6%
se 9
 
4.5%
midió 9
 
4.5%
midieron 4
 
2.0%
este 4
 
2.0%
día 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 219
15.3%
n 172
12.0%
s 168
11.7%
i 165
11.5%
a 149
10.4%
c 139
9.7%
u 128
9.0%
A 122
8.5%
r 31
 
2.2%
30
 
2.1%
Other values (8) 107
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 219
15.3%
n 172
12.0%
s 168
11.7%
i 165
11.5%
a 149
10.4%
c 139
9.7%
u 128
9.0%
A 122
8.5%
r 31
 
2.2%
30
 
2.1%
Other values (8) 107
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 219
15.3%
n 172
12.0%
s 168
11.7%
i 165
11.5%
a 149
10.4%
c 139
9.7%
u 128
9.0%
A 122
8.5%
r 31
 
2.2%
30
 
2.1%
Other values (8) 107
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 219
15.3%
n 172
12.0%
s 168
11.7%
i 165
11.5%
a 149
10.4%
c 139
9.7%
u 128
9.0%
A 122
8.5%
r 31
 
2.2%
30
 
2.1%
Other values (8) 107
7.5%

colif_fecales_ufc_100ml
Text

Distinct90
Distinct (%)53.9%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-02T20:45:27.741717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length5.1197605
Min length2

Characters and Unicode

Total characters855
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)31.1%

Sample

1st row2200
2nd row1200
3rd row1800
4th row1400
5th row1100
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
20000 6
 
3.0%
1400 5
 
2.5%
1000 5
 
2.5%
1800 5
 
2.5%
900 4
 
2.0%
3000 4
 
2.0%
40000 4
 
2.0%
Other values (84) 132
66.3%
2024-10-02T20:45:28.019988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 420
49.1%
1 57
 
6.7%
2 45
 
5.3%
32
 
3.7%
6 28
 
3.3%
i 28
 
3.3%
4 25
 
2.9%
5 23
 
2.7%
e 22
 
2.6%
3 22
 
2.6%
Other values (13) 153
 
17.9%

escher_coli_ufc_100ml
Text

Distinct79
Distinct (%)47.3%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-02T20:45:28.184061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length4.0239521
Min length1

Characters and Unicode

Total characters672
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)29.9%

Sample

1st row100
2nd row200
3rd row200
4th row100
5th row100
ValueCountFrequency (%)
100 17
 
8.5%
200 15
 
7.5%
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
300 6
 
3.0%
600 5
 
2.5%
500 4
 
2.0%
1000 4
 
2.0%
10000 4
 
2.0%
Other values (73) 110
55.3%
2024-10-02T20:45:28.451745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 273
40.6%
1 55
 
8.2%
2 36
 
5.4%
32
 
4.8%
i 28
 
4.2%
3 28
 
4.2%
5 25
 
3.7%
6 24
 
3.6%
e 22
 
3.3%
n 18
 
2.7%
Other values (13) 131
19.5%

enteroc_ufc_100ml
Text

Distinct86
Distinct (%)51.5%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-02T20:45:28.623443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.7305389
Min length1

Characters and Unicode

Total characters623
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)29.3%

Sample

1st row130
2nd row400
3rd row580
4th row300
5th row370
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
100 7
 
3.5%
300 6
 
3.0%
10 6
 
3.0%
50 6
 
3.0%
1500 5
 
2.5%
20 5
 
2.5%
2 5
 
2.5%
Other values (80) 125
62.8%
2024-10-02T20:45:28.935437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 187
30.0%
1 53
 
8.5%
5 38
 
6.1%
2 36
 
5.8%
32
 
5.1%
3 29
 
4.7%
i 28
 
4.5%
4 26
 
4.2%
e 22
 
3.5%
6 21
 
3.4%
Other values (13) 151
24.2%

nitrato_mg_l
Text

Distinct87
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:29.117871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.9404762
Min length1

Characters and Unicode

Total characters662
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)23.2%

Sample

1st row2.9
2nd row3.3
3rd row6.5
4th row7.4
5th row8.8
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
3.3 5
 
2.5%
1.9 4
 
2.0%
5.1 4
 
2.0%
5.9 4
 
2.0%
3.7 4
 
2.0%
3.9 4
 
2.0%
midieron 4
 
2.0%
Other values (81) 137
68.5%
2024-10-02T20:45:29.395586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 141
21.3%
1 60
 
9.1%
2 47
 
7.1%
3 46
 
6.9%
6 33
 
5.0%
32
 
4.8%
5 31
 
4.7%
4 30
 
4.5%
i 28
 
4.2%
8 25
 
3.8%
Other values (15) 189
28.5%

nh4_mg_l
Text

Distinct87
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:29.573740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length4.422619
Min length1

Characters and Unicode

Total characters743
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)32.1%

Sample

1st row0.42
2nd row0.51
3rd row0.05
4th row1
5th row0.049
ValueCountFrequency (%)
0.05 18
 
9.0%
no 14
 
7.0%
0.049 12
 
6.0%
se 10
 
5.0%
midió 10
 
5.0%
0.1 5
 
2.5%
0.41 5
 
2.5%
2 5
 
2.5%
1 4
 
2.0%
midieron 4
 
2.0%
Other values (80) 113
56.5%
2024-10-02T20:45:29.840478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 138
18.6%
0 134
18.0%
1 54
 
7.3%
5 41
 
5.5%
2 36
 
4.8%
4 34
 
4.6%
32
 
4.3%
9 30
 
4.0%
i 28
 
3.8%
3 24
 
3.2%
Other values (15) 192
25.8%

p_total_l_mg_l
Text

Distinct75
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:30.020408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.0238095
Min length3

Characters and Unicode

Total characters844
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)19.6%

Sample

1st row0.23
2nd row0.41
3rd row0.59
4th row0.38
5th row0.55
ValueCountFrequency (%)
no 21
 
9.8%
se 17
 
7.9%
midió 17
 
7.9%
0.33 8
 
3.7%
0.23 6
 
2.8%
0.24 5
 
2.3%
0.27 4
 
1.9%
1.2 4
 
1.9%
0.36 4
 
1.9%
midieron 4
 
1.9%
Other values (68) 124
57.9%
2024-10-02T20:45:30.361835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 147
17.4%
0 143
16.9%
3 55
 
6.5%
2 50
 
5.9%
46
 
5.5%
i 42
 
5.0%
1 41
 
4.9%
5 33
 
3.9%
4 32
 
3.8%
e 29
 
3.4%
Other values (15) 226
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 147
17.4%
0 143
16.9%
3 55
 
6.5%
2 50
 
5.9%
46
 
5.5%
i 42
 
5.0%
1 41
 
4.9%
5 33
 
3.9%
4 32
 
3.8%
e 29
 
3.4%
Other values (15) 226
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 147
17.4%
0 143
16.9%
3 55
 
6.5%
2 50
 
5.9%
46
 
5.5%
i 42
 
5.0%
1 41
 
4.9%
5 33
 
3.9%
4 32
 
3.8%
e 29
 
3.4%
Other values (15) 226
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 147
17.4%
0 143
16.9%
3 55
 
6.5%
2 50
 
5.9%
46
 
5.5%
i 42
 
5.0%
1 41
 
4.9%
5 33
 
3.9%
4 32
 
3.8%
e 29
 
3.4%
Other values (15) 226
26.8%

fosf_ortofos_mg_l
Text

Distinct68
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:30.523841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length4.7142857
Min length1

Characters and Unicode

Total characters792
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)13.7%

Sample

1st row0.15
2nd row0.35
3rd row0.54
4th row0.4
5th row0.54
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
0.32 7
 
3.5%
0.27 7
 
3.5%
0.54 6
 
3.0%
0.31 6
 
3.0%
0.18 5
 
2.5%
0.20 5
 
2.5%
0.39 5
 
2.5%
Other values (61) 125
62.5%
2024-10-02T20:45:30.802274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 163
20.6%
. 153
19.3%
2 57
 
7.2%
3 51
 
6.4%
1 44
 
5.6%
4 37
 
4.7%
32
 
4.0%
i 28
 
3.5%
5 26
 
3.3%
e 22
 
2.8%
Other values (15) 179
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 163
20.6%
. 153
19.3%
2 57
 
7.2%
3 51
 
6.4%
1 44
 
5.6%
4 37
 
4.7%
32
 
4.0%
i 28
 
3.5%
5 26
 
3.3%
e 22
 
2.8%
Other values (15) 179
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 163
20.6%
. 153
19.3%
2 57
 
7.2%
3 51
 
6.4%
1 44
 
5.6%
4 37
 
4.7%
32
 
4.0%
i 28
 
3.5%
5 26
 
3.3%
e 22
 
2.8%
Other values (15) 179
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 163
20.6%
. 153
19.3%
2 57
 
7.2%
3 51
 
6.4%
1 44
 
5.6%
4 37
 
4.7%
32
 
4.0%
i 28
 
3.5%
5 26
 
3.3%
e 22
 
2.8%
Other values (15) 179
22.6%

dbo_mg_l
Text

Distinct55
Distinct (%)32.9%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-02T20:45:30.939876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length5.8203593
Min length1

Characters and Unicode

Total characters972
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)19.2%

Sample

1st row6.2
2nd row5.8
3rd row1.9
4th row5.8
5th row2.6
ValueCountFrequency (%)
no 57
20.0%
se 53
18.6%
midió 53
18.6%
2.0 16
 
5.6%
1.9 10
 
3.5%
5 10
 
3.5%
12 5
 
1.8%
midieron 4
 
1.4%
este 4
 
1.4%
día 4
 
1.4%
Other values (47) 69
24.2%
2024-10-02T20:45:31.183057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
12.1%
i 114
11.7%
. 79
 
8.1%
e 65
 
6.7%
n 61
 
6.3%
d 61
 
6.3%
o 61
 
6.3%
m 57
 
5.9%
s 57
 
5.9%
ó 53
 
5.5%
Other values (15) 246
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
118
12.1%
i 114
11.7%
. 79
 
8.1%
e 65
 
6.7%
n 61
 
6.3%
d 61
 
6.3%
o 61
 
6.3%
m 57
 
5.9%
s 57
 
5.9%
ó 53
 
5.5%
Other values (15) 246
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
118
12.1%
i 114
11.7%
. 79
 
8.1%
e 65
 
6.7%
n 61
 
6.3%
d 61
 
6.3%
o 61
 
6.3%
m 57
 
5.9%
s 57
 
5.9%
ó 53
 
5.5%
Other values (15) 246
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
118
12.1%
i 114
11.7%
. 79
 
8.1%
e 65
 
6.7%
n 61
 
6.3%
d 61
 
6.3%
o 61
 
6.3%
m 57
 
5.9%
s 57
 
5.9%
ó 53
 
5.5%
Other values (15) 246
25.3%

dqo_mg_l
Text

Distinct51
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:31.304953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length2
Mean length3.3333333
Min length2

Characters and Unicode

Total characters560
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)17.3%

Sample

1st row29
2nd row29
3rd row29
4th row29
5th row59
ValueCountFrequency (%)
30 54
27.0%
29 27
13.5%
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
50 7
 
3.5%
midieron 4
 
2.0%
este 4
 
2.0%
día 4
 
2.0%
39 4
 
2.0%
Other values (44) 62
31.0%
2024-10-02T20:45:31.557303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 79
14.1%
0 69
12.3%
< 58
 
10.4%
9 39
 
7.0%
2 36
 
6.4%
32
 
5.7%
i 28
 
5.0%
e 22
 
3.9%
5 19
 
3.4%
4 19
 
3.4%
Other values (14) 159
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 79
14.1%
0 69
12.3%
< 58
 
10.4%
9 39
 
7.0%
2 36
 
6.4%
32
 
5.7%
i 28
 
5.0%
e 22
 
3.9%
5 19
 
3.4%
4 19
 
3.4%
Other values (14) 159
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 79
14.1%
0 69
12.3%
< 58
 
10.4%
9 39
 
7.0%
2 36
 
6.4%
32
 
5.7%
i 28
 
5.0%
e 22
 
3.9%
5 19
 
3.4%
4 19
 
3.4%
Other values (14) 159
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 79
14.1%
0 69
12.3%
< 58
 
10.4%
9 39
 
7.0%
2 36
 
6.4%
32
 
5.7%
i 28
 
5.0%
e 22
 
3.9%
5 19
 
3.4%
4 19
 
3.4%
Other values (14) 159
28.4%

turbiedad_ntu
Text

Distinct63
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-02T20:45:31.714916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length2
Mean length3.047619
Min length1

Characters and Unicode

Total characters512
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)17.3%

Sample

1st row90
2nd row34
3rd row17
4th row23
5th row18
ValueCountFrequency (%)
no 14
 
7.0%
se 10
 
5.0%
midió 10
 
5.0%
12 7
 
3.5%
19 6
 
3.0%
22 6
 
3.0%
45 6
 
3.0%
17 5
 
2.5%
23 5
 
2.5%
28 5
 
2.5%
Other values (57) 126
63.0%
2024-10-02T20:45:31.991479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 51
 
10.0%
2 50
 
9.8%
3 45
 
8.8%
5 35
 
6.8%
32
 
6.2%
i 28
 
5.5%
0 27
 
5.3%
6 22
 
4.3%
e 22
 
4.3%
7 21
 
4.1%
Other values (15) 179
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 51
 
10.0%
2 50
 
9.8%
3 45
 
8.8%
5 35
 
6.8%
32
 
6.2%
i 28
 
5.5%
0 27
 
5.3%
6 22
 
4.3%
e 22
 
4.3%
7 21
 
4.1%
Other values (15) 179
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 51
 
10.0%
2 50
 
9.8%
3 45
 
8.8%
5 35
 
6.8%
32
 
6.2%
i 28
 
5.5%
0 27
 
5.3%
6 22
 
4.3%
e 22
 
4.3%
7 21
 
4.1%
Other values (15) 179
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 51
 
10.0%
2 50
 
9.8%
3 45
 
8.8%
5 35
 
6.8%
32
 
6.2%
i 28
 
5.5%
0 27
 
5.3%
6 22
 
4.3%
e 22
 
4.3%
7 21
 
4.1%
Other values (15) 179
35.0%

hidr_deriv_petr_ug_l
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<0.10
113 
 <0.10
35 
no se midió
14 
no midieron este día
 
4
 0.10
 
1

Length

Max length20
Median length5
Mean length6.0654762
Min length5

Characters and Unicode

Total characters1019
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row<0.10
2nd row<0.10
3rd row<0.10
4th row<0.10
5th row<0.10

Common Values

ValueCountFrequency (%)
<0.10 113
67.3%
 <0.10 35
 
20.8%
no se midió 14
 
8.3%
no midieron este día 4
 
2.4%
 0.10 1
 
0.6%
 0.20 1
 
0.6%

Length

2024-10-02T20:45:32.113708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:32.226858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.10 149
71.6%
no 18
 
8.7%
se 14
 
6.7%
midió 14
 
6.7%
midieron 4
 
1.9%
este 4
 
1.9%
día 4
 
1.9%
0.20 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 300
29.4%
. 150
14.7%
1 149
14.6%
< 148
14.5%
40
 
3.9%
  37
 
3.6%
i 36
 
3.5%
e 26
 
2.6%
o 22
 
2.2%
d 22
 
2.2%
Other values (9) 89
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 300
29.4%
. 150
14.7%
1 149
14.6%
< 148
14.5%
40
 
3.9%
  37
 
3.6%
i 36
 
3.5%
e 26
 
2.6%
o 22
 
2.2%
d 22
 
2.2%
Other values (9) 89
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 300
29.4%
. 150
14.7%
1 149
14.6%
< 148
14.5%
40
 
3.9%
  37
 
3.6%
i 36
 
3.5%
e 26
 
2.6%
o 22
 
2.2%
d 22
 
2.2%
Other values (9) 89
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 300
29.4%
. 150
14.7%
1 149
14.6%
< 148
14.5%
40
 
3.9%
  37
 
3.6%
i 36
 
3.5%
e 26
 
2.6%
o 22
 
2.2%
d 22
 
2.2%
Other values (9) 89
 
8.7%

cr_total_mg_l
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<0.005
113 
no se midió
13 
0.007
 
5
6
 
4
no midieron este día
 
4
Other values (22)
29 

Length

Max length20
Median length6
Mean length6.2797619
Min length1

Characters and Unicode

Total characters1055
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)9.5%

Sample

1st row<0.005
2nd row<0.005
3rd row<0.005
4th row<0.005
5th row<0.005

Common Values

ValueCountFrequency (%)
<0.005 113
67.3%
no se midió 13
 
7.7%
0.007 5
 
3.0%
6 4
 
2.4%
no midieron este día 4
 
2.4%
0.006 3
 
1.8%
0.005 2
 
1.2%
7 2
 
1.2%
0.011 2
 
1.2%
0.0061 2
 
1.2%
Other values (17) 18
 
10.7%

Length

2024-10-02T20:45:32.353465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.005 115
55.8%
no 17
 
8.3%
se 13
 
6.3%
midió 13
 
6.3%
0.007 5
 
2.4%
6 4
 
1.9%
midieron 4
 
1.9%
este 4
 
1.9%
día 4
 
1.9%
0.006 3
 
1.5%
Other values (20) 24
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 414
39.2%
. 140
 
13.3%
5 121
 
11.5%
< 114
 
10.8%
38
 
3.6%
i 34
 
3.2%
e 25
 
2.4%
n 21
 
2.0%
d 21
 
2.0%
o 21
 
2.0%
Other values (14) 106
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 414
39.2%
. 140
 
13.3%
5 121
 
11.5%
< 114
 
10.8%
38
 
3.6%
i 34
 
3.2%
e 25
 
2.4%
n 21
 
2.0%
d 21
 
2.0%
o 21
 
2.0%
Other values (14) 106
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 414
39.2%
. 140
 
13.3%
5 121
 
11.5%
< 114
 
10.8%
38
 
3.6%
i 34
 
3.2%
e 25
 
2.4%
n 21
 
2.0%
d 21
 
2.0%
o 21
 
2.0%
Other values (14) 106
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 414
39.2%
. 140
 
13.3%
5 121
 
11.5%
< 114
 
10.8%
38
 
3.6%
i 34
 
3.2%
e 25
 
2.4%
n 21
 
2.0%
d 21
 
2.0%
o 21
 
2.0%
Other values (14) 106
 
10.0%

cd_total_mg_l
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<0.001
147 
no se midió
 
13
<0.002
 
4
no midieron este día
 
4

Length

Max length20
Median length6
Mean length6.7202381
Min length6

Characters and Unicode

Total characters1129
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<0.001
2nd row<0.001
3rd row<0.001
4th row<0.001
5th row<0.001

Common Values

ValueCountFrequency (%)
<0.001 147
87.5%
no se midió 13
 
7.7%
<0.002 4
 
2.4%
no midieron este día 4
 
2.4%

Length

2024-10-02T20:45:32.481631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:32.606746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.001 147
71.4%
no 17
 
8.3%
se 13
 
6.3%
midió 13
 
6.3%
0.002 4
 
1.9%
midieron 4
 
1.9%
este 4
 
1.9%
día 4
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 453
40.1%
< 151
 
13.4%
. 151
 
13.4%
1 147
 
13.0%
38
 
3.4%
i 34
 
3.0%
e 25
 
2.2%
o 21
 
1.9%
d 21
 
1.9%
n 21
 
1.9%
Other values (8) 67
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 453
40.1%
< 151
 
13.4%
. 151
 
13.4%
1 147
 
13.0%
38
 
3.4%
i 34
 
3.0%
e 25
 
2.2%
o 21
 
1.9%
d 21
 
1.9%
n 21
 
1.9%
Other values (8) 67
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 453
40.1%
< 151
 
13.4%
. 151
 
13.4%
1 147
 
13.0%
38
 
3.4%
i 34
 
3.0%
e 25
 
2.2%
o 21
 
1.9%
d 21
 
1.9%
n 21
 
1.9%
Other values (8) 67
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 453
40.1%
< 151
 
13.4%
. 151
 
13.4%
1 147
 
13.0%
38
 
3.4%
i 34
 
3.0%
e 25
 
2.2%
o 21
 
1.9%
d 21
 
1.9%
n 21
 
1.9%
Other values (8) 67
 
5.9%

clorofila_a_ug_l
Text

Distinct70
Distinct (%)41.9%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2024-10-02T20:45:32.789812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length3
Mean length4.2634731
Min length2

Characters and Unicode

Total characters712
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)37.1%

Sample

1st row<10
2nd row<10
3rd row<10
4th row<10
5th row<10
ValueCountFrequency (%)
10 74
36.1%
no 17
 
8.3%
se 13
 
6.3%
midió 13
 
6.3%
0.0 5
 
2.4%
midieron 4
 
2.0%
este 4
 
2.0%
día 4
 
2.0%
0.3 3
 
1.5%
350 2
 
1.0%
Other values (64) 66
32.2%
2024-10-02T20:45:33.062359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 138
19.4%
1 101
14.2%
< 74
 
10.4%
. 48
 
6.7%
38
 
5.3%
i 34
 
4.8%
2 27
 
3.8%
e 25
 
3.5%
o 21
 
2.9%
n 21
 
2.9%
Other values (15) 185
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 138
19.4%
1 101
14.2%
< 74
 
10.4%
. 48
 
6.7%
38
 
5.3%
i 34
 
4.8%
2 27
 
3.8%
e 25
 
3.5%
o 21
 
2.9%
n 21
 
2.9%
Other values (15) 185
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 138
19.4%
1 101
14.2%
< 74
 
10.4%
. 48
 
6.7%
38
 
5.3%
i 34
 
4.8%
2 27
 
3.8%
e 25
 
3.5%
o 21
 
2.9%
n 21
 
2.9%
Other values (15) 185
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 138
19.4%
1 101
14.2%
< 74
 
10.4%
. 48
 
6.7%
38
 
5.3%
i 34
 
4.8%
2 27
 
3.8%
e 25
 
3.5%
o 21
 
2.9%
n 21
 
2.9%
Other values (15) 185
26.0%

microcistina_ug_l
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<0.15
76 
 <0.15
33 
<0.20
30 
no se midió
14 
no midieron este día
 
4
Other values (10)
11 

Length

Max length20
Median length5
Mean length5.9583333
Min length1

Characters and Unicode

Total characters1001
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)5.4%

Sample

1st row<0.20
2nd row<0.20
3rd row0.2
4th row<0.20
5th row<0.20

Common Values

ValueCountFrequency (%)
<0.15 76
45.2%
 <0.15 33
19.6%
<0.20 30
 
17.9%
no se midió 14
 
8.3%
no midieron este día 4
 
2.4%
1 2
 
1.2%
0.2 1
 
0.6%
0.3 1
 
0.6%
0.4 1
 
0.6%
 0.21 1
 
0.6%
Other values (5) 5
 
3.0%

Length

2024-10-02T20:45:33.205836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.15 109
52.4%
0.20 30
 
14.4%
no 18
 
8.7%
se 14
 
6.7%
midió 14
 
6.7%
midieron 4
 
1.9%
este 4
 
1.9%
día 4
 
1.9%
1 2
 
1.0%
0.2 1
 
0.5%
Other values (8) 8
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 177
17.7%
. 148
14.8%
< 139
13.9%
1 114
11.4%
5 111
11.1%
40
 
4.0%
  37
 
3.7%
i 36
 
3.6%
2 34
 
3.4%
e 26
 
2.6%
Other values (15) 139
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177
17.7%
. 148
14.8%
< 139
13.9%
1 114
11.4%
5 111
11.1%
40
 
4.0%
  37
 
3.7%
i 36
 
3.6%
2 34
 
3.4%
e 26
 
2.6%
Other values (15) 139
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177
17.7%
. 148
14.8%
< 139
13.9%
1 114
11.4%
5 111
11.1%
40
 
4.0%
  37
 
3.7%
i 36
 
3.6%
2 34
 
3.4%
e 26
 
2.6%
Other values (15) 139
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177
17.7%
. 148
14.8%
< 139
13.9%
1 114
11.4%
5 111
11.1%
40
 
4.0%
  37
 
3.7%
i 36
 
3.6%
2 34
 
3.4%
e 26
 
2.6%
Other values (15) 139
13.9%

ica
Categorical

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)25.2%
Missing13
Missing (%)7.7%
Memory size1.4 KiB
40
13 
37
 
10
42
 
10
36
 
9
38
 
8
Other values (34)
105 

Length

Max length11
Median length2
Mean length2.0580645
Min length2

Characters and Unicode

Total characters319
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)6.5%

Sample

1st row55
2nd row42
3rd row45
4th row46
5th row44

Common Values

ValueCountFrequency (%)
40 13
 
7.7%
37 10
 
6.0%
42 10
 
6.0%
36 9
 
5.4%
38 8
 
4.8%
46 7
 
4.2%
45 7
 
4.2%
41 7
 
4.2%
39 7
 
4.2%
44 6
 
3.6%
Other values (29) 71
42.3%
(Missing) 13
 
7.7%

Length

2024-10-02T20:45:33.318670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40 13
 
8.3%
37 10
 
6.4%
42 10
 
6.4%
36 9
 
5.7%
38 8
 
5.1%
46 7
 
4.5%
45 7
 
4.5%
41 7
 
4.5%
39 7
 
4.5%
44 6
 
3.8%
Other values (31) 73
46.5%

Most occurring characters

ValueCountFrequency (%)
4 71
22.3%
3 59
18.5%
5 53
16.6%
6 26
 
8.2%
0 21
 
6.6%
2 19
 
6.0%
9 17
 
5.3%
8 14
 
4.4%
7 14
 
4.4%
1 14
 
4.4%
Other values (9) 11
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 71
22.3%
3 59
18.5%
5 53
16.6%
6 26
 
8.2%
0 21
 
6.6%
2 19
 
6.0%
9 17
 
5.3%
8 14
 
4.4%
7 14
 
4.4%
1 14
 
4.4%
Other values (9) 11
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 71
22.3%
3 59
18.5%
5 53
16.6%
6 26
 
8.2%
0 21
 
6.6%
2 19
 
6.0%
9 17
 
5.3%
8 14
 
4.4%
7 14
 
4.4%
1 14
 
4.4%
Other values (9) 11
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 71
22.3%
3 59
18.5%
5 53
16.6%
6 26
 
8.2%
0 21
 
6.6%
2 19
 
6.0%
9 17
 
5.3%
8 14
 
4.4%
7 14
 
4.4%
1 14
 
4.4%
Other values (9) 11
 
3.4%

calidad_de_agua
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)1.9%
Missing14
Missing (%)8.3%
Memory size1.4 KiB
Extremadamente deteriorada
92 
Muy deteriorada
60 
Deteriorada
 
2

Length

Max length26
Median length26
Mean length21.519481
Min length11

Characters and Unicode

Total characters3314
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMuy deteriorada
2nd rowExtremadamente deteriorada
3rd rowMuy deteriorada
4th rowMuy deteriorada
5th rowExtremadamente deteriorada

Common Values

ValueCountFrequency (%)
Extremadamente deteriorada 92
54.8%
Muy deteriorada 60
35.7%
Deteriorada 2
 
1.2%
(Missing) 14
 
8.3%

Length

2024-10-02T20:45:33.436424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-02T20:45:33.529221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
deteriorada 154
50.3%
extremadamente 92
30.1%
muy 60
 
19.6%

Most occurring characters

ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 584
17.6%
a 492
14.8%
r 400
12.1%
d 398
12.0%
t 338
10.2%
m 184
 
5.6%
i 154
 
4.6%
o 154
 
4.6%
152
 
4.6%
E 92
 
2.8%
Other values (6) 366
11.0%

Interactions

2024-10-02T20:45:23.204433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-02T20:45:33.604939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
añocalidad_de_aguacampañacd_total_mg_lcodigocolorcr_total_mg_lespumasfechahidr_deriv_petr_ug_licamat_suspmicrocistina_ug_loloresordensitiostem_aire
año1.0001.0000.9910.7240.0000.7270.6200.7270.9880.7131.0000.7230.6740.7270.3020.0000.584
calidad_de_agua1.0001.0000.1350.2410.2580.1580.0000.1730.1100.2450.8760.1720.2070.1960.1400.2580.191
campaña0.9910.1351.0000.6190.0000.4940.4630.4840.9970.6210.2930.4820.7190.4890.0880.0000.539
cd_total_mg_l0.7240.2410.6191.0000.2860.7360.7230.7350.6140.7700.4640.7330.7340.7360.4510.2860.625
codigo0.0000.2580.0000.2861.0000.4460.0880.4530.0000.0810.0000.4000.0000.4000.8931.0000.154
color0.7270.1580.4940.7360.4461.0000.5640.9160.4900.5570.4690.8970.5530.9210.4650.4460.647
cr_total_mg_l0.6200.0000.4630.7230.0880.5641.0000.5570.3950.5530.0000.5140.2470.6070.2260.0880.000
espumas0.7270.1730.4840.7350.4530.9160.5571.0000.4810.5560.5180.8980.5200.9180.4590.4530.622
fecha0.9880.1100.9970.6140.0000.4900.3950.4811.0000.6160.2810.4380.6470.4850.1330.0000.616
hidr_deriv_petr_ug_l0.7130.2450.6210.7700.0810.5570.5530.5560.6161.0000.4240.5590.7300.5590.2740.0810.474
ica1.0000.8760.2930.4640.0000.4690.0000.5180.2810.4241.0000.4070.0000.5160.0000.0000.000
mat_susp0.7230.1720.4820.7330.4000.8970.5140.8980.4380.5590.4071.0000.4700.8980.4200.4000.612
microcistina_ug_l0.6740.2070.7190.7340.0000.5530.2470.5200.6470.7300.0000.4701.0000.5260.1880.0000.471
olores0.7270.1960.4890.7360.4000.9210.6070.9180.4850.5590.5160.8980.5261.0000.4520.4000.630
orden0.3020.1400.0880.4510.8930.4650.2260.4590.1330.2740.0000.4200.1880.4521.0000.8930.476
sitios0.0000.2580.0000.2861.0000.4460.0880.4530.0000.0810.0000.4000.0000.4000.8931.0000.154
tem_aire0.5840.1910.5390.6250.1540.6470.0000.6220.6160.4740.0000.6120.4710.6300.4760.1541.000

Missing values

2024-10-02T20:45:23.342646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-02T20:45:23.649873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-02T20:45:23.851862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ordensitioscodigofechaañocampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
01Canal Villanueva y Río LujánTI00123/2/20222022Verano24.523.35.36.56AusenciaAusenciaAusenciaPresencia22001001302.90.420.230.156.22990<0.10<0.005<0.001<10<0.2055Muy deteriorada
12Río Lujan y Arroyo CaraguatáTI00623/2/20222022Verano25.423.32.256.56PresenciaPresenciaAusenciaAusencia12002004003.30.510.410.355.82934<0.10<0.005<0.001<10<0.2042Extremadamente deteriorada
23Canal Aliviador y Río LujanTI00223/2/20222022Verano24.623.32.946.59AusenciaPresenciaAusenciaAusencia18002005806.50.050.590.541.92917<0.10<0.005<0.001<100.245Muy deteriorada
34Río Carapachay y Arroyo Gallo FiambreTI00323/2/20222022Verano25.223.32.227.45PresenciaPresenciaAusenciaAusencia14001003007.410.380.45.82923<0.10<0.005<0.001<10<0.2046Muy deteriorada
45Río Reconquista y Río LujanTI00423/2/20222022Verano24.1201.026.39AusenciaPresenciaAusenciaPresencia11001003708.80.0490.550.542.65918<0.10<0.005<0.001<10<0.2044Extremadamente deteriorada
56Rio Tigre 100m antes del Rio LujánTI00523/2/20222022Verano24.923.33.56.53AusenciaAusenciaAusenciaPresencia32002007504.43.51.10.913.91308.9<0.10<0.005<0.001<10<0.2040Extremadamente deteriorada
67Río Lujan y Canal San FernandoTI00723/2/20222022Verano24.5201.56.54AusenciaPresenciaAusenciaPresencia1800015001005.620.730.63.54212<0.10<0.005<0.001<100.435Extremadamente deteriorada
78Río Capitán y Río San AntonioTI00823/2/20222022Verano24.5216.36.48AusenciaPresenciaAusenciaAusencia100020012003.10.0490.170.165.56990<0.10<0.005<0.001<10<0.2046Muy deteriorada
89Arroyo Abra Vieja y Santa RosaTI00923/2/20222022Verano23.4214.496.76AusenciaAusenciaAusenciaAusencia4001002201.90.10.210.191.92939<0.10<0.005<0.001<10<0.2058Muy deteriorada
910Del ArcaSF01523/2/20222022Verano21.5233.856.66AusenciaAusenciaAusenciaPresencia22001002705.40.0490.280.391.92928<0.10<0.005<0.001<10<0.2051Muy deteriorada
ordensitioscodigofechaañocampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_lclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
15833Boca Cerrada (Res.Nat. Punta Lara)EN-extra31/10/20222022Primaverano se midió10no se midióno se midióAusenciaAusenciaAusenciaAusencia150100306.20.140.600.38no se midió7239<0.1010<0.00174.2<0.1541Extremadamente deteriorada
15934Camping Eva PerónEN08131/10/20222022Primavera16711.059,17AusenciaAusenciaAusenciaAusencia21080905.90.150.360.21no se midió3331<0.10<0.005<0.00136.50.1938Extremadamente deteriorada
16035Toma de agua Club de PescaEN08231/10/20222022Primavera17.268.388,09AusenciaAusenciaAusenciaAusencia9535505.70.410.290.23no se midió4626<0.10<0.005<0.00129.4<0.1541Extremadamente deteriorada
16136Arroyo El GatoEN08331/10/02022022Primavera1847.367,87AusenciaAusenciaAusenciaAusencia8007002205.42.30.360.23no se midió<3023<0.10<0.005<0.00116.7<0.1537Extremadamente deteriorada
16237Ensenada Prefectura Isla SantiagoEN08431/10/02022022Primavera17.158.988,05AusenciaAusenciaAusenciaAusencia13030456.10.40.240.24no se midió<3039<0.10<0.005<0.0010.6<0.1554Muy deteriorada
16338Balneario Palo BlancoBS09231/10/20222022Primavera1012no se midióno se midióAusenciaAusenciaAusenciaPresencia8006004006.90.380.240.24no se midió<3023<0.10<0.005<0.0012.1<0.1543Extremadamente deteriorada
16439Diagonal 66 (descarga cloaca)BS09531/10/20222022Primavera1012no se midióno se midióAusenciaPresenciaAusenciaPresencia8000080000120005.21.230.120.39no se midió3118.2<0.10<0.005<0.00120.2<0.1537Extremadamente deteriorada
16540Playa La BagliardiBS09131/10/20222022Primavera1012no se midióno se midióAusenciaAusenciaAusenciaPresencia140010003804.60.80.450.43no se midió<3040<0.10<0.005<0.0010.2<0.1549Muy deteriorada
16641Balneario MunicipalBS09431/10/20222022Primavera1012no se midióno se midióAusenciaAusenciaAusenciaPresencia180015005005.20.550.270.27no se midió3990<0.105<0.00110.5<0.1539Extremadamente deteriorada
16742Playa La BalandraBS09331/10/20222022Primavera1012no se midióno se midióAusenciaAusenciaAusenciaPresencia9006004805.10.210.480.35no se midió<3070<0.105<0.00148.0<0.1534Extremadamente deteriorada